Datasets:
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Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1901, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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2 0.660609 0.408238 0.055795 0.041846 |
1 0.622350 0.443508 0.098836 0.087279 |
2 0.503188 0.416009 0.088474 0.065758 |
2 0.417105 0.433046 0.090069 0.077116 |
1 0.361709 0.457855 0.079707 0.075323 |
2 0.611383 0.345180 0.119826 0.108252 |
1 0.489107 0.353181 0.099321 0.098039 |
2 0.359349 0.362793 0.096117 0.085544 |
2 0.434000 0.320261 0.081379 0.086986 |
2 0.607985 0.278879 0.132140 0.117754 |
0 0.349744 0.337489 0.142796 0.133738 |
1 0.701407 0.336157 0.088804 0.088448 |
2 0.478332 0.293265 0.111537 0.108163 |
0 0.595156 0.427672 0.123030 0.110534 |
2 0.463476 0.388745 0.119826 0.101884 |
1 0.348434 0.409060 0.110998 0.086207 |
2 0.654455 0.388631 0.059053 0.060200 |
2 0.325234 0.459506 0.132140 0.092178 |
2 0.430023 0.425938 0.117221 0.090047 |
2 0.550440 0.399297 0.109406 0.083653 |
2 0.372549 0.479167 0.113290 0.116830 |
2 0.487473 0.485703 0.110022 0.111928 |
2 0.606209 0.488971 0.110022 0.108660 |
1 0.376455 0.514646 0.117169 0.077714 |
1 0.588076 0.468018 0.092460 0.095648 |
2 0.483660 0.484756 0.102025 0.087279 |
1 0.691993 0.493260 0.126634 0.100082 |
2 0.576195 0.433996 0.106639 0.072669 |
2 0.468410 0.456140 0.079119 0.069659 |
2 0.315044 0.462375 0.095746 0.078689 |
1 0.315653 0.494910 0.160885 0.099925 |
2 0.441344 0.430178 0.108932 0.073529 |
2 0.654600 0.448089 0.093012 0.072901 |
1 0.740070 0.485168 0.044411 0.045249 |
2 0.461240 0.423792 0.097279 0.084929 |
1 0.283402 0.459986 0.127679 0.104879 |
2 0.310002 0.443456 0.100319 0.079799 |
1 0.470740 0.453431 0.123879 0.116849 |
3 0.572959 0.394722 0.091199 0.060990 |
2 0.660739 0.420657 0.060040 0.033060 |
1 0.673659 0.501881 0.081319 0.073529 |
0 0.479480 0.479366 0.136799 0.121979 |
1 0.326721 0.473951 0.132239 0.095189 |
2 0.649719 0.440606 0.074479 0.047880 |
3 0.447940 0.387027 0.104119 0.045600 |
1 0.715323 0.448597 0.029956 0.031999 |
0 0.675006 0.482815 0.102922 0.093532 |
0 0.315485 0.515711 0.090777 0.079186 |
2 0.475082 0.389706 0.116013 0.082721 |
1 0.366930 0.464525 0.133012 0.098469 |
1 0.619482 0.412496 0.104919 0.100189 |
1 0.547529 0.510535 0.088293 0.074389 |
1 0.685988 0.420881 0.090013 0.052460 |
0 0.305011 0.523080 0.122004 0.100082 |
2 0.345861 0.454861 0.147059 0.136029 |
0 0.531469 0.483055 0.102881 0.077766 |
2 0.446968 0.382229 0.104658 0.103003 |
2 0.605816 0.395725 0.094730 0.079114 |
2 0.648631 0.441176 0.081079 0.093696 |
2 0.710681 0.448778 0.081079 0.092765 |
3 0.332108 0.367034 0.085784 0.060662 |
2 0.337010 0.350797 0.000000 0.000000 |
3 0.676879 0.376991 0.076797 0.058517 |
0 0.559829 0.450038 0.109100 0.095400 |
1 0.369434 0.455346 0.156761 0.118719 |
1 0.711806 0.465686 0.095997 0.079657 |
1 0.658701 0.394646 0.123570 0.116039 |
2 0.364583 0.359796 0.120507 0.083869 |
1 0.486050 0.387511 0.114623 0.107185 |
0 0.406863 0.507966 0.118301 0.098284 |
1 0.519935 0.459314 0.103922 0.085294 |
2 0.394771 0.437745 0.111765 0.057353 |
1 0.639052 0.480760 0.071569 0.049755 |
2 0.634641 0.445098 0.074510 0.047549 |
3 0.486765 0.396324 0.068954 0.046569 |
2 0.581155 0.415986 0.073711 0.055828 |
0 0.619281 0.606443 0.074230 0.070028 |
1 0.562558 0.611345 0.094304 0.071429 |
3 0.387021 0.584559 0.086835 0.062675 |
3 0.476657 0.581232 0.065359 0.066527 |
2 0.569561 0.557598 0.067227 0.052171 |
1 0.552920 0.491698 0.100148 0.080645 |
1 0.401645 0.498221 0.088552 0.068390 |
0 0.361241 0.533380 0.095964 0.079365 |
0 0.613082 0.520542 0.087146 0.079365 |
1 0.548242 0.495837 0.071584 0.065749 |
1 0.434641 0.512761 0.084034 0.070806 |
0 0.563582 0.599974 0.114666 0.095889 |
1 0.372090 0.600189 0.088293 0.072239 |
2 0.458376 0.572884 0.096893 0.075249 |
3 0.561391 0.535627 0.062092 0.043592 |
1 0.444791 0.577057 0.105466 0.077986 |
2 0.352694 0.565174 0.058923 0.057932 |
2 0.575758 0.500557 0.076253 0.049762 |
2 0.548524 0.549577 0.087146 0.045306 |
3 0.430184 0.512069 0.061398 0.041592 |
3 0.637651 0.545677 0.040602 0.073901 |
1 0.630719 0.528443 0.109336 0.077766 |
2 0.541757 0.531923 0.053256 0.089567 |
2 0.381990 0.520879 0.109739 0.101368 |
Dataset Deteksi Tandan Buah Segar (TBS) Kelapa Sawit - YOLO Format
Dataset citra lapangan pohon kelapa sawit dari dua varietas di Indonesia: Damimas (Blok A21B) dan Lonsum (Blok A21A). Dataset ini menggunakan format YOLO untuk deteksi objek dengan 4 kelas tingkat kematangan buah: B1, B2, B3, dan B4.
Split yang dipublikasikan di sini mengikuti split kanonik lokal yang dipakai pada workflow autoresearch untuk YOLO.
Ringkasan Dataset
| Split | Jumlah Gambar | Persentase |
|---|---|---|
| Train | 2,764 | 69.2% |
| Validation | 604 | 15.1% |
| Test | 624 | 15.6% |
| Total | 3,992 | 100% |
- Total gambar: 3,992 JPG
- Total label: 3,992 TXT (format YOLO)
- Total pohon/sekuens: 953
- Varietas: 2 (Damimas, Lonsum)
- Kelas: 4 (
B1,B2,B3,B4)
Struktur Folder
Dataset-YOLO/
|-- images/
| |-- train/
| |-- val/
| `-- test/
|-- labels/
| |-- train/
| |-- val/
| `-- test/
|-- data.yaml
|-- LICENSE
`-- README.md
Semua gambar memiliki file label pasangan dengan nama stem yang sama.
Konfigurasi data.yaml
path: .
train: images/train
val: images/val
test: images/test
nc: 4
names:
0: B1
1: B2
2: B3
3: B4
Format Label YOLO
Setiap file .txt berisi satu baris per bounding box:
<class_id> <x_center> <y_center> <width> <height>
Contoh:
2 0.456789 0.345678 0.123456 0.234567
1 0.678901 0.789012 0.098765 0.087654
Keterangan kelas:
0 = B11 = B22 = B33 = B4
Konvensi Penamaan File
Format nama file:
{VARIETAS}_{BLOK}_{NOMOR_POHON}_{SISI_FOTO}.jpg
Contoh:
DAMIMAS_A21B_0001_1.jpg
DAMIMAS_A21B_0001_1.txt
Satu pohon difoto dari beberapa sisi, dan seluruh sisi dari pohon yang sama berada pada split yang sama.
Penggunaan
Ultralytics YOLO
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model.train(data="data.yaml", epochs=100, imgsz=640)
model.val(data="data.yaml")
Hugging Face datasets
from datasets import load_dataset
dataset = load_dataset("ULM-DS-Lab/Dataset-Sawit-YOLO")
train_ds = dataset["train"]
val_ds = dataset["validation"]
test_ds = dataset["test"]
Catatan
- Split train/val/test pada repo ini bersifat fixed.
- Split ini diselaraskan dengan dataset lokal kanonik yang dipakai pada workflow autoresearch YOLO.
- Jika Anda ingin eksperimen dengan split lain, lakukan resplit di salinan dataset terpisah.
Lisensi
Proprietary. Lihat file LICENSE.
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